Data mining: The process of extracting useful information from a huge amount of data is called Data mining. Data mining is a tool that is used by humans to discover new, accurate, and useful patterns in data or meaningful relevant information for the ones who need it.
Machine learning: The process of discovering algorithms that have improved courtesy of experience derived data is known as machine learning. It is the algorithm that permits the machine to learn without human intervention. It’s a tool to make machines smarter, eliminating the human element.
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S.No. | Data Mining | Machine Learning |
---|---|---|
1. | Extracting useful information from large amount of data | Introduce algorithm from data as well as from past experience |
2. | Used to understand the data flow | Teaches the computer to learn and understand from the data flow |
3. | Huge databases with unstructured data | Existing data as well as algorithms |
4. | Models can be developed for using data mining technique | machine learning algorithm can be used in the decision tree, neural networks and some other area of artificial intelligence |
5. | human interference is more in it. | No human effort required after design |
6. | It is used in cluster analysis | It is used in web Search, spam filter, fraud detection and computer design |
7. | Data mining abstract from the data warehouse | Machine learning reads machine |
8. | Data mining is more of a research using methods like machine learning | Self learned and trains system to do the intelligent task |
9. | Applied in limited area | Can be used in vast area |
10. | Uncovering hidden patterns and insights | Making accurate predictions or decisions based on data |
11. | Exploratory and descriptive | Predictive and prescriptive |
12. | Historical data | Historical and real-time data |
13. | Patterns, relationships, and trends | Predictions, classifications, and recommendations |
14. | Clustering, association rule mining, outlier detection | Regression, classification, clustering, deep learning |
15. | Data cleaning, transformation, and integration | Data cleaning, transformation, and feature engineering |
16. | Strong domain knowledge is often required | Domain knowledge is helpful, but not always necessary |
17. | Can be used in a wide range of applications, including business, healthcare, and social science | Primarily used in applications where prediction or decision-making is important, such as finance, manufacturing, and cybersecurity |